Zeng Jing, Cao Xian-Fen, Chen Jian, Liu Zhi-Ping, Lyu Jun, Zhou Qing
Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Ophthalmic Center, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
Clin Exp Ophthalmol. 2024 Dec;52(9):991-1002. doi: 10.1111/ceo.14427. Epub 2024 Aug 1.
Accurate prognostic factors for primary ocular adnexal lymphoma (POAL) are scarce. Survival models and prognostic factors derived without considering competing risk factors suffer from major statistical errors. This study aimed to accurately assess prognostic factors in POAL patients using competing risk models, and compare this to the traditional COX proportional hazards model.
This retrospective study utilised data from the Surveillance, Epidemiology, and End Results (SEER) program 2010-2015 and included patients with B-cell POAL. The cumulative incidence function and Gray's test for cause-specific survival were calculated as univariate analysis. The competing risk models were a Fine-Gray subdistribution hazard model and a cause-specific model, and a traditional COX model was employed as a multivariate analysis.
This study enrolled 846 eligible patients with POAL: 60 patients (7.09%) died from POAL and 123 patients (14.54%) died from other causes. Multivariate competing risk models indicated that age, laterality, histology subtype, the 7th edition of American Joint Committee on Cancer stage T, and radiotherapy were independent predictors for cause-specific survival of patients with POAL. There was high consistency between the two competing risk models. The COX model made several misestimations on the statistical significance and hazard ratios of prognostic factors.
This study established competing risk models as a method to assess POAL prognostic factors, which was more accurate than traditional methods that do not consider competing risk elements.
原发性眼附属器淋巴瘤（POAL）的准确预后因素较为缺乏。在不考虑竞争风险因素的情况下得出的生存模型和预后因素存在重大统计误差。本研究旨在使用竞争风险模型准确评估POAL患者的预后因素，并将其与传统的COX比例风险模型进行比较。
这项回顾性研究利用了2010 - 2015年监测、流行病学和最终结果（SEER）计划的数据，纳入了B细胞POAL患者。计算累积发病率函数和特定病因生存的Gray检验作为单变量分析。竞争风险模型为Fine - Gray子分布风险模型和特定病因模型，并采用传统的COX模型进行多变量分析。
本研究纳入了846例符合条件的POAL患者：60例（7.09%）死于POAL，123例（14.54%）死于其他原因。多变量竞争风险模型表明，年龄、病变侧别、组织学亚型、美国癌症联合委员会第7版分期T以及放疗是POAL患者特定病因生存的独立预测因素。两种竞争风险模型之间具有高度一致性。COX模型对预后因素的统计学意义和风险比存在一些错误估计。
本研究建立了竞争风险模型作为评估POAL预后因素的方法，该方法比不考虑竞争风险因素的传统方法更准确。
